
df_2024[(df_2024['order_date']>='2024-11-01') & (df_2024['order_date']<='2024-12-04')]['user_id'].nunique()

5201


df_2024['traffic_type'] = df_2024['traffic_type'].map({1: 'web', 2: 'extension', 3: 'mobile'})


df_2024['traffic_type'].value_counts()

traffic_type
mobile       251410
extension    183768
web           34183
Name: count, dtype: int64


df_merged


df_merged['%_extension_orders'] = df_merged['order_count_x'] / df_merged['all'] * 100


df_merged[['month', '%_extension_orders']]


df_merged['%_extension_orders'].median()

39.194290937794534


sns.barplot(x=df_merged['month'], y=df_merged['%_extension_orders'])
plt.title('etxension traffic % out of total by month')

Text(0.5, 1.0, 'etxension traffic % out of total by month')


df_grouped_2024


data = [go.Scatter(x=df_grouped_2024['month'], y=df_grouped_2024['customer_value '], name='customer_value')]
layout = go.Layout(barmode='stack', title= 'Customer value by month Aliexpress Int.', xaxis_title='month', yaxis_title='US dollars$')
fig = go.Figure(data=data, layout=layout)
fig.show()


df_grouped_2024['customer_value '].std()

1.6555642641805814


data = [go.Scatter(x=df_grouped_2024['month'], y=df_grouped_2024['avg_purchase_value'], name='avg_purchase_value')]
layout = go.Layout(barmode='stack', title= 'avg_purchase_value by month Aliexpress Int.', xaxis_title='month', yaxis_title='revenue $ per order')
fig = go.Figure(data=data, layout=layout)
fig.show()


data = [go.Scatter(x=df_grouped_2024['month'], y=df_grouped_2024['avg_purchase_frequency_rate'], name='purchase_frequency')]
layout = go.Layout(barmode='stack', title= 'purchase_frequency by month Aliexpress Int.', xaxis_title='month', yaxis_title='orders_per_user')
fig = go.Figure(data=data, layout=layout)
fig.show()


data = [go.Scatter(x=df_grouped_2024['month'], y=df_grouped_2024['avg_purchase'], name='avg_purchase')]
layout = go.Layout(barmode='stack', title= 'avg_purchase by month Aliexpress Int.', xaxis_title='month', yaxis_title='US dollars$ per order')
fig = go.Figure(data=data, layout=layout)
fig.show()


data = [go.Scatter(x=df_grouped_2024['month'], y=df_grouped_2024['user_count'], name='user_count')]
layout = go.Layout(barmode='stack', title= 'user_count by month Aliexpress Int.', xaxis_title='month', yaxis_title='users_count')
fig = go.Figure(data=data, layout=layout)
fig.show()


data = [go.Scatter(x=df_grouped_2024['month'], y=df_grouped_2024['avg_user_cashback'], name='cashback USD $')]
layout = go.Layout(barmode='stack', title= 'avg_user_cashback', xaxis_title='month', yaxis_title='cashback USD $')
fig = go.Figure(data=data, layout=layout)
fig.show()


df_grouped_nov_2024


data = [go.Scatter(x=df_grouped_nov_2024['order_date'], y=df_grouped_nov_2024['customer_value '], name='customer_value')]
layout = go.Layout(barmode='stack', title= 'Customer value by day Aliexpress Int.', xaxis_title='day', yaxis_title='US dollars$')
fig = go.Figure(data=data, layout=layout)
fig.show()


data = [go.Scatter(x=df_grouped_nov_2024['order_date'], y=df_grouped_nov_2024['user_count'], name='user_count')]
layout = go.Layout(barmode='stack', title= 'user_count by day', xaxis_title='day', yaxis_title='users_count')
fig = go.Figure(data=data, layout=layout)
fig.show()


df_grouped_nov_2024['customer_value '].mean()

3.2504030610403056


df_grouped_nov_2024['avg_purchase_value'].mean()

0.8246803401108906


df_grouped_nov_2024['avg_purchase_frequency_rate'].mean()

3.9146806295207623


df_grouped_nov_2024['avg_purchase'].mean()

12.671238307172448


df_grouped_nov_2024['avg_user_cashback'].mean()

2.0993163666110415


df_grouped_country_2024[['country', 'user_count', 'revenue_sum', 'avg_purchase_value', 'avg_purchase_frequency_rate','avg_purchase', 'avg_user_cashback', 'customer_value' ]].sort_values(by='revenue_sum', ascending=False).head(10).reset_index()


df_2024[df_2024['country']=='VN'].groupby('month').agg(GMV=('GMV', 'sum'), Revenue=('Revenue', 'sum'), order_count = ('order_number', 'nunique'), user_count=('user_id', 'nunique'))


df_2024[df_2024['country']=='BR'].groupby('month').agg(GMV=('GMV', 'sum'), Revenue=('Revenue', 'sum'), order_count = ('order_number', 'nunique'), user_count=('user_id', 'nunique'))


df_2024[df_2024['country']=='MX'].groupby('month').agg(GMV=('GMV', 'sum'), Revenue=('Revenue', 'sum'), order_count = ('order_number', 'nunique'), user_count=('user_id', 'nunique'))


df_grouped_country_nov_2024[['country', 'user_count', 'revenue_sum', 'avg_purchase_value', 'avg_purchase_frequency_rate','avg_purchase', 'avg_user_cashback', 'customer_value' ]].sort_values(by='revenue_sum', ascending=False).head(10).reset_index()


df_grouped_level_2024 = df_2024.groupby(['level']).agg(user_count = ('user_id', 'nunique'), order_count = ('order_number', 'nunique'), 
                               GMV_sum = ('GMV', 'sum'), revenue_sum = ('Revenue', 'sum'), 
                               user_com_sum = ('users_comission', 'sum')).reset_index()


df_grouped_level_2024


data = [go.Scatter(x=df_grouped_level_2024['level'], y=df_grouped_level_2024['customer_value'], name='customer_value')]
layout = go.Layout(barmode='stack', title= 'Customer value by lvl Aliexpress Int.', xaxis_title='level', yaxis_title='US dollars$')
fig = go.Figure(data=data, layout=layout)
fig.show()


df_grouped_level_nov_2024


df_2024_all['order_number'].nunique() / df_2024_all['activations'].nunique()

0.29913421430824777


df_conversion_2024 = df_2024_all.groupby(['month']).agg(order_count=('order_number', 'nunique'), activation_count=('activations', 'nunique')).reset_index()


df_conversion_2024['%_conversion'] = df_conversion_2024['order_count'] / df_conversion_2024['activation_count'] * 100


df_conversion_2024


sns.barplot(x=df_conversion_2024['month'], y=df_conversion_2024['%_conversion'])
plt.title('%_Conversion')

Text(0.5, 1.0, '%_Conversion')


df_conversion_2024[df_conversion_2024['month'] != 9]['%_conversion'].mean()

35.50050875002477


df_conversion_nov_2024 = df_2024_all[(df_2024_all['activation_date']>='2024-11-01') & (df_2024_all['activation_date']<='2024-12-04')].groupby(['activation_date']).agg(order_count=('order_number', 'nunique'), activation_count=('activations', 'nunique')).reset_index()


df_conversion_nov_2024['%_conversion'] = df_conversion_nov_2024['order_count'] / df_conversion_nov_2024['activation_count'] * 100


df_conversion_nov_2024


data = [go.Scatter(x=df_conversion_nov_2024['activation_date'], y=df_conversion_nov_2024['%_conversion'], name='conversion')]
layout = go.Layout(barmode='stack', title= '%_conversion_Nov', xaxis_title='days', yaxis_title='%')
fig = go.Figure(data=data, layout=layout)
fig.show()


df_conversion_nov_2024['%_conversion'].mean()

33.7715521557841


df_2024_all[(df_2024_all['reg_date'] >= '2024-11-01') & (df_2024_all['reg_date'] <= '2024-12-04')]['user_id'].nunique()

873


873 / 5201 * 100

16.78523360892136


df_new_users_nov_2024


df_new_users_nov_2024['avg_purchase_value'].mean()

0.9659705411245065


df_new_users_nov_2024['avg_purchase_frequency_rate'].mean()

3.1488933452168744


df_new_users_nov_2024['customer_value'].mean()

3.05252003342246


df_2024[df_2024['cashback_link_count'] >= 1]['user_id'].drop_duplicates()

1         2584994
12        3100571
17         840196
18        2444755
24         843295
           ...   
459657    1508985
462375    3031548
463409     298967
468286    1307418
468701    1243889
Name: user_id, Length: 2970, dtype: int64


df_2024[df_2024['cashback_link_count'] >= 50]['user_id'].drop_duplicates()

17         840196
414         25794
662        856755
766       2652988
826       2444191
           ...   
412928    2786363
415143    2364298
417981     611529
420320    2875573
420377    2476058
Name: user_id, Length: 167, dtype: int64


df_2024[(df_2024['cashback_link_count'].notna()) & (df_2024['order_date']>= '2024-11-01') 
        & (df_2024['order_date']<= '2024-12-04')]['user_id'].nunique()

1539


1539 / 2970 * 100

51.81818181818182


df_grouped_cb_link


df_grouped_cb_link['revenue_sum'].sum() / df_grouped_2024['revenue_sum'].sum() * 100

38.91915094740444


df_reactivated[(df_reactivated['date']>='2024-11-01') & (df_reactivated['date']<='2024-12-04')]['user_id'].nunique()

3623


df_2024_nov = df_2024[(df_2024['order_date']>='2024-11-01') & (df_2024['order_date']<='2024-12-04')]


df_2024_nov[df_2024_nov['user_id'].isin(df_reactivated['user_id'])]['user_id'].nunique()

671


671 / 3623 *100

18.520563069279603


df_reactivated_grouped


df_reactivated_grouped['avg_purchase_value'].mean()

0.8553155422693444


df_reactivated_grouped['avg_purchase_frequency_rate'].mean()

3.4451775939799845


df_reactivated_grouped['customer_value '].mean()

2.8796629356619583


df_2023[(df_2023['order_date']>='2023-11-01') & (df_2023['order_date']<='2023-12-04')]['user_id'].nunique()

6596


df_2023['traffic_type'] = df_2023['traffic_type'].map({1: 'web', 2: 'extension', 3: 'mobile'})


df_2023['traffic_type'].value_counts()

traffic_type
mobile       398060
extension    331256
web           73200
Name: count, dtype: int64


df_merged_2023[['month', '%_extension_orders']]


df_merged_2023['%_extension_orders'].median()

40.4628515173149


sns.barplot(x=df_merged_2023['month'], y=df_merged_2023['%_extension_orders'])
plt.title('etxension traffic % out of total by month')

Text(0.5, 1.0, 'etxension traffic % out of total by month')


df_grouped_2023


data = [go.Scatter(x=df_grouped_2023['month'], y=df_grouped_2023['customer_value '], name='customer_value 2023'),
       go.Scatter(x=df_grouped_2024['month'], y=df_grouped_2024['customer_value '], name='customer_value 2024')]
layout = go.Layout(barmode='stack', title= 'Customer value by month 2023-2024 Aliexpress Int.', xaxis_title='month', yaxis_title='US dollars$')
fig = go.Figure(data=data, layout=layout)
fig.show()


data = [go.Scatter(x=df_grouped_2023['month'], y=df_grouped_2023['avg_purchase_value'], name='avg_purchase_value 2023'),
    go.Scatter(x=df_grouped_2024['month'], y=df_grouped_2024['avg_purchase_value'], name='avg_purchase_value 2024')]
layout = go.Layout(barmode='stack', title= 'avg_purchase_value by month 2023-2024 Aliexpress Int.', xaxis_title='month', yaxis_title='revenue $ per order')
fig = go.Figure(data=data, layout=layout)
fig.show()


data = [go.Scatter(x=df_grouped_2023['month'], y=df_grouped_2023['avg_purchase_frequency_rate'], name='purchase_frequency 2023'),
    go.Scatter(x=df_grouped_2024['month'], y=df_grouped_2024['avg_purchase_frequency_rate'], name='purchase_frequency 2024')]
layout = go.Layout(barmode='stack', title= 'purchase_frequency by month 2023-2024 Aliexpress Int.', xaxis_title='month', yaxis_title='orders_per_user')
fig = go.Figure(data=data, layout=layout)
fig.show()


data = [go.Scatter(x=df_grouped_2023['month'], y=df_grouped_2023['user_count'], name='user_count 2023'),
    go.Scatter(x=df_grouped_2024['month'], y=df_grouped_2024['user_count'], name='user_count 2024')]
layout = go.Layout(barmode='stack', title= 'user_count by month 2023-2024', xaxis_title='month', yaxis_title='users_count')
fig = go.Figure(data=data, layout=layout)
fig.show()


df_grouped_2023['customer_value '].mean()

8.180213752125367


df_grouped_2024['customer_value '].mean()

7.472812971837596


df_grouped_2023['avg_purchase_value'].mean()

0.7050777648273072


df_grouped_2024['avg_purchase_value'].mean()

0.792967914358269


df_grouped_2023['avg_purchase_frequency_rate'].mean()

11.96716650020908


df_grouped_2024['avg_purchase_frequency_rate'].mean()

9.801491461611164


df_grouped_2023['avg_purchase'].mean()

10.866352275766758


df_grouped_2024['avg_purchase'].mean()

12.579711707814027


df_grouped_2023['avg_user_cashback'].mean()

5.576503449349789


df_grouped_2024['avg_user_cashback'].mean()

4.967450845356287


df_grouped_2023['user_count'].mean()

5472.583333333333


df_grouped_2024['user_count'].mean()

3942.8333333333335


df_grouped_nov_2023


df_grouped_nov_2023['customer_value '].mean()

3.0870659634391338


df_grouped_nov_2024['customer_value '].mean()

3.25048700120043


df_grouped_nov_2023['avg_purchase_value'].mean()

0.7134803555602464


df_grouped_nov_2024['avg_purchase_value'].mean()

0.8245389216618724


df_grouped_nov_2023['avg_purchase_frequency_rate'].mean()

4.308474727048903


df_grouped_nov_2024['avg_purchase_frequency_rate'].mean()

3.9154669290678035


df_grouped_nov_2023['avg_purchase'].mean()

11.029459390786267


df_grouped_nov_2024['avg_purchase'].mean()

12.669058470814145


df_grouped_nov_2023['avg_user_cashback'].mean()

2.02492765808567


df_grouped_nov_2024['avg_user_cashback'].mean()

2.0993557764665214


df_grouped_nov_2023['user_count'].mean()

679.6764705882352


df_grouped_nov_2024['user_count'].mean()

547.6176470588235


df_2023[(df_2023['reg_date'] >= '2023-11-01') & (df_2023['reg_date'] <= '2023-12-04')]['user_id'].nunique()

267


267 / 6596 * 100

4.0479078229229835


df_new_users_nov_2023


df_new_users_nov_2023['customer_value '].mean()

2.8198821165505774


df_new_users_nov_2023['avg_purchase_value'].mean()

0.7063326295389718


df_new_users_nov_2023['avg_purchase_frequency_rate'].mean()

8.830179295138027


df_2023[df_2023['cashback_link_count'] >= 50]['user_id'].drop_duplicates()

136        118119
140       2698085
142       1462568
171         32558
303         25794
           ...   
720283    1058008
728090     126200
728880    3061279
764875     191729
790718    1948844
Name: user_id, Length: 213, dtype: int64


df_2023[df_2023['cashback_link_count'].notna()]['user_id'].nunique()

3622


df_2023[(df_2023['cashback_link_count'].notna()) & (df_2023['order_date']>= '2023-11-01') 
        & (df_2023['order_date']<= '2023-12-04')]['user_id'].nunique()

2004


2004 / 3622 * 100

55.32854776366648


df_grouped_cb_link_2023 = df_2023[df_2023['cashback_link_count'].notna()].groupby(['month']).agg(user_count = ('user_id', 'nunique'), order_count = ('order_number', 'nunique'), 
                               GMV_sum = ('GMV', 'sum'), revenue_sum = ('Revenue', 'sum'), 
                               user_com_sum = ('users_comission', 'sum')).reset_index()


df_grouped_cb_link_2023


df_grouped_cb_link_2023['revenue_sum'].sum() / df_grouped_2023['revenue_sum'].sum() * 100

41.594374745631605


df_reactivated_2023


df_2023_nov = df_2023[(df_2023['order_date']>='2023-11-01') & (df_2023['order_date']<='2023-12-04')]


df_2023_nov[df_2023_nov['user_id'].isin(df_reactivated_2023['user_id'])]['user_id'].nunique()

1002


df_reactivated_grouped_2023


df_reactivated_grouped_2023['avg_purchase_value'].mean()

0.7136830015944523


df_reactivated_grouped_2023['avg_purchase_frequency_rate'].mean()

3.5925332068427105


df_reactivated_grouped_2023['customer_value '].mean()

2.5343682566475447

	traffic_type_x	month	order_count_x	traffic_type_y	order_count_y	traffic_type	order_count	all
0	extension	1	20755	mobile	29695	web	6335	56785
1	extension	2	14373	mobile	26217	web	6919	47509
2	extension	3	19180	mobile	28932	web	5081	53193
3	extension	4	15586	mobile	20474	web	2488	38548
4	extension	5	15122	mobile	21085	web	3634	39841
5	extension	6	16303	mobile	24779	web	4714	45796
6	extension	7	15546	mobile	24810	web	1513	41869
7	extension	8	10896	mobile	12520	web	700	24116
8	extension	9	4040	mobile	1896	web	177	6113
9	extension	10	12148	mobile	11545	web	443	24136
10	extension	11	28323	mobile	35749	web	1647	65719
11	extension	12	11496	mobile	13708	web	532	25736

	month	%_extension_orders
0	1	36.550145
1	2	30.253215
2	3	36.057376
3	4	40.432707
4	5	37.955875
5	6	35.599179
6	7	37.130096
7	8	45.181622
8	9	66.088664
9	10	50.331455
10	11	43.097126
11	12	44.668946

	month	user_count	order_count	GMV_sum	revenue_sum	user_com_sum	gross_profit	avg_purchase_value	avg_purchase_frequency_rate	avg_purchase	avg_user_cashback	customer_value	%_extension_orders
0	1	5062	56785	646799.47	42300.2018	27804.91	14495.2918	0.744919	11.217898	11.390323	5.492870	8.356421	36.550145
1	2	4807	47509	537838.44	34459.7634	22533.13	11926.6334	0.725331	9.883295	11.320770	4.687566	7.168663	30.253215
2	3	5068	53193	679871.12	43584.9846	28127.27	15457.7146	0.819374	10.495856	12.781214	5.549974	8.600036	36.057376
3	4	4271	38548	452335.58	30001.6523	19387.95	10613.7023	0.778293	9.025521	11.734346	4.539440	7.024503	40.432707
4	5	4099	39841	486751.74	31877.7708	22355.86	9521.9108	0.800125	9.719688	12.217357	5.453979	7.776963	37.955875
5	6	4253	45796	609174.27	39205.9725	25306.66	13899.3125	0.856100	10.767929	13.301910	5.950308	9.218428	35.599179
6	7	4106	41869	490049.93	32381.9898	20807.88	11574.1098	0.773412	10.197029	11.704362	5.067677	7.886505	37.130096
7	8	3139	24116	287857.89	18570.5987	11962.98	6607.6187	0.770053	7.682701	11.936386	3.811080	5.916088	45.181622
8	9	1209	6113	103274.45	6251.9939	3952.14	2299.8539	1.022737	5.056245	16.894234	3.268933	5.171211	66.088664
9	10	3111	24136	288795.38	18966.6800	12312.81	6653.8700	0.785825	7.758277	11.965337	3.957830	6.096651	50.331455
10	11	4922	65719	859812.46	55472.0602	35796.58	19675.4802	0.844079	13.352093	13.083164	7.272771	11.270228	43.097126
11	12	3206	25736	326705.25	21388.0434	14273.59	7114.4534	0.831055	8.027449	12.694484	4.452149	6.671255	44.668946

	order_date	user_count	order_count	GMV_sum	revenue_sum	user_com_sum	gross_profit	avg_purchase_value	avg_purchase_frequency_rate	customer_value	avg_purchase	avg_user_cashback
0	2024-11-01	552	2555	44383.03	2920.8789	1900.18	1020.6989	1.143201	4.628623	5.291447	17.371049	3.442355
1	2024-11-02	477	2038	26434.39	1715.6732	1132.20	583.4732	0.841842	4.272537	3.596799	12.970751	2.373585
2	2024-11-03	476	1837	19139.68	1253.9784	811.31	442.6684	0.682623	3.859244	2.634408	10.418987	1.704433
3	2024-11-04	452	1674	20800.21	1340.5757	870.71	469.8657	0.800822	3.703540	2.965875	12.425454	1.926350
4	2024-11-05	458	1940	20747.72	1371.3541	892.01	479.3441	0.706884	4.235808	2.994223	10.694701	1.947620
5	2024-11-06	488	1888	24879.45	1619.1201	1066.17	552.9501	0.857585	3.868852	3.317869	13.177675	2.184775
6	2024-11-07	517	2127	25003.49	1650.9630	1063.11	587.8530	0.776193	4.114120	3.193352	11.755284	2.056306
7	2024-11-08	382	1643	20054.38	1282.2445	838.66	443.5845	0.780429	4.301047	3.356661	12.205953	2.195445
8	2024-11-09	280	989	9046.71	620.7892	411.37	209.4192	0.627694	3.532143	2.217104	9.147331	1.469179
9	2024-11-10	268	738	7327.60	487.9713	308.79	179.1813	0.661208	2.753731	1.820788	9.928997	1.152201
10	2024-11-11	1162	5740	88409.43	5356.6748	3449.69	1906.9848	0.933219	4.939759	4.609875	15.402340	2.968752
11	2024-11-12	855	3725	53302.79	3268.1107	2083.10	1185.0107	0.877345	4.356725	3.822352	14.309474	2.436374
12	2024-11-13	678	2825	37134.13	2438.8557	1571.40	867.4557	0.863312	4.166667	3.597132	13.144825	2.317699
13	2024-11-14	663	2604	38446.39	2461.1553	1601.82	859.3353	0.945144	3.927602	3.712150	14.764359	2.416018
14	2024-11-15	620	2536	33419.94	2090.6588	1345.44	745.2188	0.824392	4.090323	3.372030	13.178210	2.170065
15	2024-11-16	640	2520	31000.22	2025.3883	1302.96	722.4283	0.803726	3.937500	3.164669	12.301675	2.035875
16	2024-11-17	613	2473	25174.95	1652.2856	1073.57	578.7156	0.668130	4.034258	2.695409	10.179923	1.751338
17	2024-11-18	734	3311	41313.79	2688.8351	1734.00	954.8351	0.812092	4.510899	3.663263	12.477738	2.362398
18	2024-11-19	547	2252	30807.00	2065.1926	1330.56	734.6326	0.917048	4.117002	3.775489	13.679840	2.432468
19	2024-11-20	281	888	9257.74	646.2642	416.18	230.0842	0.727775	3.160142	2.299873	10.425383	1.481068
20	2024-11-21	266	804	8140.11	556.6090	349.56	207.0490	0.692300	3.022556	2.092515	10.124515	1.314135
21	2024-11-22	720	2931	42776.81	2797.2064	1763.57	1033.6364	0.954352	4.070833	3.885009	14.594613	2.449403
22	2024-11-23	578	2138	22354.01	1520.0412	975.00	545.0412	0.710964	3.698962	2.629829	10.455571	1.686851
23	2024-11-24	521	2033	22714.85	1468.9637	950.26	518.7037	0.722560	3.902111	2.819508	11.173069	1.823916
24	2024-11-25	540	2026	24654.91	1598.3096	1035.10	563.2096	0.788899	3.751852	2.959833	12.169255	1.916852
25	2024-11-26	518	1840	27770.22	1774.2354	1137.15	637.0854	0.964258	3.552124	3.425165	15.092511	2.195270
26	2024-11-27	491	1656	25286.27	1527.8300	990.13	537.7000	0.922603	3.372709	3.111670	15.269487	2.016558
27	2024-11-28	501	1827	22452.71	1502.7149	971.26	531.4549	0.822504	3.646707	2.999431	12.289387	1.938643
28	2024-11-29	555	2062	27208.57	1799.6940	1140.63	659.0640	0.872790	3.715315	3.242692	13.195233	2.055189
29	2024-11-30	517	2099	30370.96	1969.4865	1280.69	688.7965	0.938298	4.059961	3.809452	14.469252	2.477157
30	2024-12-01	523	2036	23161.96	1499.3510	964.81	534.5410	0.736420	3.892925	2.866828	11.376208	1.844761
31	2024-12-02	580	2218	29815.54	1908.8587	1228.91	679.9487	0.860622	3.824138	3.291136	13.442534	2.118810
32	2024-12-03	635	2779	38241.09	2510.3645	1625.04	885.3245	0.903334	4.376378	3.953330	13.760738	2.559118
33	2024-12-04	537	1988	26738.17	1786.3506	1157.66	628.6906	0.898567	3.702048	3.326537	13.449784	2.155791

	index	country	user_count	revenue_sum	avg_purchase_value	avg_purchase_frequency_rate	avg_purchase	avg_user_cashback	customer_value
0	130	UA	2668	80586.2569	1.019021	29.640930	15.672755	20.033205	30.204744
1	139	VN	90	24945.1800	0.776963	356.733333	11.061949	186.647222	277.168667
2	21	BR	203	24345.2100	1.050449	114.167488	15.032255	76.946847	119.927143
3	58	IL	766	22895.4272	0.691496	43.224543	10.284111	19.390144	29.889592
4	17	BG	641	15360.4500	0.780630	30.697348	11.726527	15.552637	23.963261
5	76	LT	581	11942.1800	0.779465	26.370052	11.525819	13.282031	20.554527
6	78	LV	688	9720.7350	0.579754	24.370640	8.893707	9.016701	14.128975
7	90	MX	209	9421.6220	1.110647	40.588517	16.920905	29.321531	45.079531
8	133	US	310	7329.1798	0.539505	43.822581	8.503602	14.890000	23.642515
9	40	ES	462	7032.5100	0.498937	30.508658	7.788748	9.980866	15.221883

Анализ ноябрьской распродажи в оффере Aliexpress Int.¶

Процент заказов через расширение от общего трафика¶

Группировка данных по метрикам по месяцам за 2024 год Aliexpress Int.¶

Сгруппируем те же данные, но по дням во время Ноябрьской распродажи с 2024-11-01 по 2024-12-04, чтобы посмотреть какая была динамика по дням¶

Сгрупируем те же данные по странам, чтобы оценить эффективность каждой страны как канала продаж. Посмотрим ситуацию за весь год, а также отдельно по Ноябрю.¶

Сгрупируем те же данные по уровню пользователя и посмотрим показатели пользователей с разным уровнем.¶

Конверсия из активации в покупку¶

Доля новых юзеров и их поведение¶

Поведение новых пользователей во время Ноябрьской распродажи.¶

Влияние кэшбек ссылок¶

Реактивированные пользователи¶

2023¶

Процент заказов через расширение от общего трафика¶

Сгруппируем те же данные, но по дням во время Ноябрьской распродажи с 2023-11-01 по 2023-12-04, чтобы посмотреть какая была динамика по дням¶

Доля новых юзеров и их поведение¶

Поведение новых пользователей¶

Влияние кэшбек ссылок¶

Реактивированные¶

	GMV	Revenue	order_count	user_count
month
1	11537.38	799.96	725	37
2	19542.08	1344.95	1399	44
3	46823.60	3250.32	4041	48
4	40789.27	2800.18	3671	45
5	42157.97	2875.77	3754	49
6	31118.48	2156.20	2791	41
7	22137.21	1539.01	2312	43
8	28300.72	1980.58	2617	48
9	19404.55	1404.80	1715	39
10	31568.10	2243.58	2881	51
11	40468.87	3020.31	4031	47
12	21306.70	1529.52	2169	41

	GMV	Revenue	order_count	user_count
month
1	18566.20	1275.27	620	38
2	50657.92	3355.00	2064	68
3	41193.87	3000.73	2695	78
4	29356.47	2145.50	2136	68
5	31111.35	2283.36	2141	63
6	64872.00	4416.14	4599	77
7	41209.72	3029.93	3855	92
8	16033.43	1145.61	1847	55
9	3493.36	239.59	259	24
10	16316.47	1100.16	1177	60
11	25178.00	1647.60	1422	70
12	10398.76	706.32	361	51

	GMV	Revenue	order_count	user_count
month
1	4234.25	278.790	230	34
2	15755.18	1082.850	961	70
3	22571.98	1430.300	1291	73
4	11438.97	699.220	618	63
5	18331.41	1120.870	818	75
6	19783.22	1305.230	1103	83
7	9085.02	614.360	782	74
8	7919.79	546.340	613	67
9	1057.24	75.260	54	16
10	3630.69	251.240	344	44
11	19568.04	1316.872	1126	74
12	10164.25	700.290	543	50

	index	country	user_count	revenue_sum	avg_purchase_value	avg_purchase_frequency_rate	avg_purchase	avg_user_cashback	customer_value
0	106	UA	1207	13531.0246	1.088490	10.299089	16.826017	7.335551	11.210459
1	48	IL	413	3823.4872	0.672912	13.757869	9.990111	5.851719	9.257838
2	111	VN	48	3446.2400	0.725830	98.916667	9.730666	45.886250	71.796667
3	12	BG	307	2405.7300	0.845302	9.270358	12.289803	4.994919	7.836254
4	62	LT	280	2405.6900	1.007408	8.528571	15.025791	5.522107	8.591750
5	16	BR	75	1816.0500	1.161900	20.840000	17.790774	16.010533	24.214000
6	74	MX	84	1803.2320	1.238484	17.333333	18.254258	14.167262	21.467048
7	108	US	95	1425.6800	0.687075	21.842105	10.606761	8.814737	15.007158
8	64	LV	286	1286.2100	0.560929	8.017483	8.600375	2.794196	4.497238
9	87	PS	20	1175.1200	0.942358	62.350000	13.854747	40.016500	58.756000

	level	user_count	order_count	GMV_sum	revenue_sum	user_com_sum	gross_profit	avg_purchase_value	avg_purchase_frequency_rate	avg_purchase	avg_user_cashback	customer_value
0	1	1345	7049	87792.12	5440.5807	2711.21	2729.3707	0.771823	5.240892	12.454550	2.015770	4.045041
1	2	419	3314	27080.26	1548.5895	783.38	765.2095	0.467287	7.909308	8.171473	1.869642	3.695918
2	3	678	6690	55158.19	3353.7778	1763.23	1590.5478	0.501312	9.867257	8.244871	2.600634	4.946575
3	4	1588	22552	185508.43	11454.7221	6390.56	5064.1621	0.507925	14.201511	8.225808	4.024282	7.213301
4	5	4038	119035	1034312.94	66406.6345	40690.59	25716.0445	0.557875	29.478702	8.689150	10.076917	16.445427
5	6	2248	174286	2422365.97	155845.9034	102729.98	53115.9234	0.894196	77.529359	13.898798	45.698390	69.326469
6	7	1150	136435	1957048.07	130411.5034	89552.81	40858.6934	0.955851	118.639130	14.344179	77.872009	113.401307

	level	user_count	order_count	GMV_sum	revenue_sum	user_com_sum	gross_profit	avg_purchase_value	avg_purchase_frequency_rate	customer_value
0	1	448	2956	40851.36	2618.8825	1287.91	1330.9725	0.885955	6.598214	5.845720
1	2	89	791	8315.30	480.7561	248.41	232.3461	0.607783	8.887640	5.401754
2	3	158	1284	12687.82	784.7429	438.86	345.8829	0.611170	8.126582	4.966727
3	4	520	3591	33103.82	1994.4176	1149.77	844.6476	0.555393	6.905769	3.835418
4	5	1976	21219	202172.89	12971.9967	7971.74	5000.2567	0.611339	10.738360	6.564776
5	6	1425	28191	406462.65	25925.5178	17003.05	8922.4678	0.919638	19.783158	18.193346
6	7	585	16708	274175.38	18400.6714	12673.26	5727.4114	1.101309	28.560684	31.454139

	month	order_count	activation_count	%_conversion
0	1	57085	128557	44.404428
1	2	47485	108871	43.615839
2	3	53116	125001	42.492460
3	4	38582	111153	34.710714
4	5	40990	98825	41.477359
5	6	44637	113511	39.323942
6	7	41952	83074	50.499555
7	8	23858	156818	15.213815
8	9	6145	194312	3.162440
9	10	24552	173282	14.168812
10	11	65557	192488	34.057707
11	12	25411	83203	30.540966

	activation_date	order_count	activation_count	%_conversion
0	2024-11-01	2525	6562	38.479122
1	2024-11-02	1966	5690	34.551845
2	2024-11-03	1847	5629	32.812222
3	2024-11-04	1765	6094	28.962914
4	2024-11-05	1894	5750	32.939130
5	2024-11-06	1869	6013	31.082654
6	2024-11-07	2168	5696	38.061798
7	2024-11-08	1754	5368	32.675112
8	2024-11-09	1274	4822	26.420572
9	2024-11-10	1312	5110	25.675147
10	2024-11-11	5739	11779	48.722302
11	2024-11-12	3161	9216	34.299045
12	2024-11-13	2627	7463	35.200322
13	2024-11-14	2514	7362	34.148329
14	2024-11-15	2432	6848	35.514019
15	2024-11-16	2495	6786	36.766873
16	2024-11-17	2602	6413	40.573834
17	2024-11-18	3294	7549	43.634919
18	2024-11-19	2267	6374	35.566363
19	2024-11-20	1069	5300	20.169811
20	2024-11-21	1246	5093	24.464952
21	2024-11-22	2523	7529	33.510426
22	2024-11-23	1906	5748	33.159360
23	2024-11-24	2027	5986	33.862345
24	2024-11-25	1883	6284	29.964990
25	2024-11-26	1751	5910	29.627750
26	2024-11-27	1712	6633	25.810342
27	2024-11-28	1811	5917	30.606726
28	2024-11-29	2041	5935	34.389217
29	2024-11-30	2083	5629	37.004797
30	2024-12-01	2071	5491	37.716263
31	2024-12-02	2330	6151	37.880020
32	2024-12-03	2721	6455	42.153369
33	2024-12-04	1879	5904	31.825881

	order_date	user_count	order_count	GMV_sum	revenue_sum	user_com_sum	gross_profit	avg_purchase_value	avg_purchase_frequency_rate	avg_purchase	avg_user_cashback	customer_value
0	2024-11-01	2	6	79.59	3.8100	1.76	2.0500	0.635000	3.000000	13.265000	0.880000	1.905000
1	2024-11-02	1	3	1.73	0.1100	0.06	0.0500	0.036667	3.000000	0.576667	0.060000	0.110000
2	2024-11-03	3	6	94.10	6.5600	3.28	3.2800	1.093333	2.000000	15.683333	1.093333	2.186667
3	2024-11-04	6	15	131.04	9.0900	4.19	4.9000	0.606000	2.500000	8.736000	0.698333	1.515000
4	2024-11-05	1	1	13.51	0.9400	0.50	0.4400	0.940000	1.000000	13.510000	0.500000	0.940000
5	2024-11-06	2	8	182.33	12.3600	5.93	6.4300	1.545000	4.000000	22.791250	2.965000	6.180000
6	2024-11-07	1	1	0.04	0.0100	0.01	0.0000	0.010000	1.000000	0.040000	0.010000	0.010000
7	2024-11-08	4	11	119.86	8.3300	3.97	4.3600	0.757273	2.750000	10.896364	0.992500	2.082500
8	2024-11-09	2	7	38.41	2.5800	1.18	1.4000	0.368571	3.500000	5.487143	0.590000	1.290000
9	2024-11-10	4	4	22.51	1.5100	0.71	0.8000	0.377500	1.000000	5.627500	0.177500	0.377500
10	2024-11-11	15	42	2288.87	88.7662	48.20	40.5662	2.113481	2.800000	54.496905	3.213333	5.917747
11	2024-11-12	9	37	584.69	35.3023	16.46	18.8423	0.954116	4.111111	15.802432	1.828889	3.922478
12	2024-11-13	10	27	465.85	32.1900	17.30	14.8900	1.192222	2.700000	17.253704	1.730000	3.219000
13	2024-11-14	8	30	610.18	38.8607	19.03	19.8307	1.295357	3.750000	20.339333	2.378750	4.857588
14	2024-11-15	9	28	1271.46	19.8918	9.97	9.9218	0.710421	3.111111	45.409286	1.107778	2.210200
15	2024-11-16	4	23	442.66	30.8300	14.73	16.1000	1.340435	5.750000	19.246087	3.682500	7.707500
16	2024-11-17	3	9	71.98	5.0000	2.55	2.4500	0.555556	3.000000	7.997778	0.850000	1.666667
17	2024-11-18	11	46	1508.19	71.4600	34.14	37.3200	1.553478	4.181818	32.786739	3.103636	6.496364
18	2024-11-19	5	19	214.09	15.7300	7.69	8.0400	0.827895	3.800000	11.267895	1.538000	3.146000
19	2024-11-20	5	10	62.44	4.3200	2.19	2.1300	0.432000	2.000000	6.244000	0.438000	0.864000
20	2024-11-21	3	3	13.19	0.9100	0.41	0.5000	0.303333	1.000000	4.396667	0.136667	0.303333
21	2024-11-22	10	51	535.31	36.3700	17.22	19.1500	0.713137	5.100000	10.496275	1.722000	3.637000
22	2024-11-23	10	51	386.61	26.4400	12.52	13.9200	0.518431	5.100000	7.580588	1.252000	2.644000
23	2024-11-24	10	26	241.09	15.0500	7.69	7.3600	0.578846	2.600000	9.272692	0.769000	1.505000
24	2024-11-25	7	22	236.93	16.2200	7.97	8.2500	0.737273	3.142857	10.769545	1.138571	2.317143
25	2024-11-26	7	29	442.88	24.7900	11.85	12.9400	0.854828	4.142857	15.271724	1.692857	3.541429
26	2024-11-27	9	24	511.80	35.3700	18.81	16.5600	1.473750	2.666667	21.325000	2.090000	3.930000
27	2024-11-28	4	25	369.03	25.0500	12.66	12.3900	1.002000	6.250000	14.761200	3.165000	6.262500
28	2024-11-29	7	19	769.52	53.6300	25.97	27.6600	2.822632	2.714286	40.501053	3.710000	7.661429
29	2024-11-30	8	26	168.01	11.8000	5.53	6.2700	0.453846	3.250000	6.461923	0.691250	1.475000
30	2024-12-01	10	20	108.28	7.5100	3.72	3.7900	0.375500	2.000000	5.414000	0.372000	0.751000
31	2024-12-02	8	15	1168.67	67.1900	35.34	31.8500	4.479333	1.875000	77.911333	4.417500	8.398750
32	2024-12-03	5	23	145.22	10.0300	4.95	5.0800	0.436087	4.600000	6.313913	0.990000	2.006000
33	2024-12-04	9	33	312.40	24.7400	10.99	13.7500	0.749697	3.666667	9.466667	1.221111	2.748889

	month	user_count	order_count	GMV_sum	revenue_sum	user_com_sum
0	1	1631	22742	263414.97	16847.2035	11222.44
1	2	1487	17515	213214.91	13097.1802	8743.79
2	3	1569	21231	293722.47	18028.2521	11925.85
3	4	1326	15261	186305.53	12003.6766	7987.00
4	5	1288	16070	191307.36	12372.0372	8913.93
5	6	1378	18827	251265.30	16057.6140	10594.55
6	7	1353	16496	200545.87	13133.5880	8618.58
7	8	1016	9097	106585.57	6918.8783	4488.88
8	9	397	2197	39556.22	2289.5742	1474.28
9	10	978	7828	99965.95	6605.0102	4330.61
10	11	1483	22268	325042.92	20978.9063	13742.89
11	12	994	8654	114603.43	7405.3981	5027.87

	order_date	user_count	order_count	GMV_sum	revenue_sum	user_com_sum	gross_profit	avg_purchase_value	avg_purchase_frequency_rate	avg_purchase	avg_user_cashback	customer_value
0	2024-11-01	45	185	2285.36	148.9488	92.03	56.9188	0.805129	4.111111	12.353297	2.045111	3.309973
1	2024-11-02	48	191	2863.60	201.2288	129.71	71.5188	1.053554	3.979167	14.992670	2.702292	4.192267
2	2024-11-03	37	155	1358.12	77.7438	48.95	28.7938	0.501573	4.189189	8.762065	1.322973	2.101184
3	2024-11-04	23	66	2123.67	156.5800	100.80	55.7800	2.372424	2.869565	32.176818	4.382609	6.807826
4	2024-11-05	49	169	2147.15	143.3129	99.82	43.4929	0.848005	3.448980	12.705030	2.037143	2.924753
5	2024-11-06	44	114	2650.84	152.5600	97.96	54.6000	1.338246	2.590909	23.252982	2.226364	3.467273
6	2024-11-07	42	188	1512.49	90.7798	55.38	35.3998	0.482871	4.476190	8.045160	1.318571	2.161424
7	2024-11-08	40	159	1579.53	99.5242	60.37	39.1542	0.625938	3.975000	9.934151	1.509250	2.488105
8	2024-11-09	25	75	794.79	52.6955	33.84	18.8555	0.702607	3.000000	10.597200	1.353600	2.107820
9	2024-11-10	18	52	318.63	22.0400	13.36	8.6800	0.423846	2.888889	6.127500	0.742222	1.224444
10	2024-11-11	111	566	8787.22	422.7193	261.78	160.9393	0.746854	5.099099	15.525124	2.358378	3.808282
11	2024-11-12	82	354	4526.46	278.7402	167.59	111.1502	0.787402	4.317073	12.786610	2.043780	3.399271
12	2024-11-13	51	233	2836.17	193.5732	120.81	72.7632	0.830786	4.568627	12.172403	2.368824	3.795553
13	2024-11-14	53	172	1544.83	107.6340	64.64	42.9940	0.625779	3.245283	8.981570	1.219623	2.030830
14	2024-11-15	60	170	2230.47	147.2729	90.80	56.4729	0.866311	2.833333	13.120412	1.513333	2.454548
15	2024-11-16	51	170	2051.63	140.4863	87.71	52.7763	0.826390	3.333333	12.068412	1.719804	2.754633
16	2024-11-17	62	229	2249.57	137.2043	85.42	51.7843	0.599145	3.693548	9.823450	1.377742	2.212973
17	2024-11-18	61	195	2330.68	154.8256	96.40	58.4256	0.793977	3.196721	11.952205	1.580328	2.538125
18	2024-11-19	42	169	3389.57	188.1850	118.02	70.1650	1.113521	4.023810	20.056627	2.810000	4.480595
19	2024-11-20	22	60	1304.38	91.3000	57.17	34.1300	1.521667	2.727273	21.739667	2.598636	4.150000
20	2024-11-21	23	42	627.64	41.7000	25.59	16.1100	0.992857	1.826087	14.943810	1.112609	1.813043
21	2024-11-22	55	182	2702.64	171.8356	100.45	71.3856	0.944152	3.309091	14.849670	1.826364	3.124284
22	2024-11-23	50	126	935.73	63.5326	38.90	24.6326	0.504227	2.520000	7.426429	0.778000	1.270652
23	2024-11-24	39	110	1088.50	65.2286	41.02	24.2086	0.592987	2.820513	9.895455	1.051795	1.672528
24	2024-11-25	52	177	1858.45	118.7942	75.69	43.1042	0.671154	3.403846	10.499718	1.455577	2.284504
25	2024-11-26	47	136	2288.33	147.6982	91.50	56.1982	1.086016	2.893617	16.825956	1.946809	3.142515
26	2024-11-27	50	170	1805.18	116.4543	71.65	44.8043	0.685025	3.400000	10.618706	1.433000	2.329086
27	2024-11-28	51	180	2729.20	182.9670	113.25	69.7170	1.016483	3.529412	15.162222	2.220588	3.587588
28	2024-11-29	57	176	2377.06	152.0656	94.64	57.4256	0.864009	3.087719	13.506023	1.660351	2.667818
29	2024-11-30	59	226	2976.12	160.1995	98.87	61.3295	0.708847	3.830508	13.168673	1.675763	2.715246
30	2024-12-01	41	121	1582.17	80.0971	48.15	31.9471	0.661960	2.951220	13.075785	1.174390	1.953588
31	2024-12-02	62	171	2966.18	184.8400	119.41	65.4300	1.080936	2.758065	17.346082	1.925968	2.981290
32	2024-12-03	66	316	3703.49	261.0138	154.60	106.4138	0.825993	4.787879	11.719905	2.342424	3.954755
33	2024-12-04	51	176	1525.15	102.0900	63.88	38.2100	0.580057	3.450980	8.665625	1.252549	2.001765

	month	%_extension_orders
0	1	41.021773
1	2	46.372393
2	3	46.986265
3	4	49.890100
4	5	48.698517
5	6	39.091226
6	7	40.235263
7	8	39.948159
8	9	36.002355
9	10	34.167870
10	11	33.407768
11	12	40.690440

	month	user_count	order_count	GMV_sum	revenue_sum	user_com_sum	gross_profit	avg_purchase_value	avg_purchase_frequency_rate	avg_purchase	avg_user_cashback	customer_value	%_extension_orders
0	1	5877	67608	703485.01	44598.3318	29549.91	15048.4218	0.659661	11.503828	10.405352	5.028060	7.588622	41.021773
1	2	5470	65925	642077.07	41021.4449	27230.29	13791.1549	0.622244	12.052102	9.739508	4.978115	7.499350	46.372393
2	3	6076	79287	873952.73	55014.4512	36404.55	18609.9012	0.693865	13.049210	11.022648	5.991532	9.054386	46.986265
3	4	5454	65969	599583.39	39053.7410	25740.07	13313.6710	0.592001	12.095526	9.088866	4.719485	7.160569	49.890100
4	5	5429	67692	650864.42	42151.3327	27644.60	14506.7327	0.622693	12.468595	9.615086	5.092024	7.764106	48.698517
5	6	5294	60213	689243.34	43839.4580	28712.05	15127.4080	0.728073	11.373819	11.446753	5.423508	8.280971	39.091226
6	7	5195	59848	679347.16	44291.5128	29320.81	14970.7028	0.740067	11.520308	11.351209	5.644044	8.525796	40.235263
7	8	5241	65585	835783.68	52247.2596	34569.64	17677.6196	0.796634	12.513833	12.743519	6.596001	9.968949	39.948159
8	9	5290	62857	698229.50	45023.8921	29966.92	15056.9721	0.716291	11.882231	11.108222	5.664824	8.511133	36.002355
9	10	5171	60273	636366.69	41778.8720	27629.48	14149.3920	0.693161	11.655966	10.558072	5.343160	8.079457	34.167870
10	11	6396	98073	1129618.64	71880.9938	47051.59	24829.4038	0.732934	15.333490	11.518141	7.356409	11.238429	33.407768
11	12	4778	49186	580338.29	36738.4886	24276.44	12462.0486	0.746930	10.294265	11.798851	5.080879	7.689093	40.690440

	order_date	user_count	order_count	GMV_sum	revenue_sum	user_com_sum	gross_profit	avg_purchase_value	avg_purchase_frequency_rate	avg_purchase	avg_user_cashback	customer_value
0	2023-11-01	592	2438	26216.49	1777.7440	1182.91	594.8340	0.729181	4.118243	10.753277	1.998159	3.002946
1	2023-11-02	559	2520	29252.59	1916.2716	1271.53	644.7416	0.760425	4.508050	11.608171	2.274651	3.428035
2	2023-11-03	519	2005	21005.58	1359.1410	890.05	469.0910	0.677876	3.863198	10.476599	1.714933	2.618769
3	2023-11-04	479	2048	22457.40	1525.0829	1012.39	512.6929	0.744669	4.275574	10.965527	2.113549	3.183889
4	2023-11-05	392	1303	11757.68	784.5350	520.74	263.7950	0.602099	3.323980	9.023546	1.328418	2.001365
5	2023-11-06	472	1901	21703.86	1472.8573	970.32	502.5373	0.774780	4.027542	11.417075	2.055763	3.120460
6	2023-11-07	470	1876	19884.34	1307.1045	870.12	436.9845	0.696751	3.991489	10.599328	1.851319	2.781073
7	2023-11-08	463	2002	20006.12	1317.4219	877.96	439.4619	0.658053	4.323974	9.993067	1.896242	2.845404
8	2023-11-09	397	2005	17117.96	1185.0003	750.85	434.1503	0.591023	5.050378	8.537636	1.891310	2.984887
9	2023-11-10	358	1494	16931.66	1199.8690	766.74	433.1290	0.803125	4.173184	11.333106	2.141732	3.351589
10	2023-11-11	1772	10694	138002.40	8401.7828	5344.34	3057.4428	0.785654	6.034989	12.904657	3.015993	4.741412
11	2023-11-12	1256	6746	72469.75	4490.0658	2906.16	1583.9058	0.665589	5.371019	10.742625	2.313822	3.574893
12	2023-11-13	1037	5390	50100.34	3191.4578	2121.31	1070.1478	0.592107	5.197686	9.295054	2.045622	3.077587
13	2023-11-14	904	4398	47084.96	2981.8632	1986.70	995.1632	0.678004	4.865044	10.705994	2.197677	3.298521
14	2023-11-15	933	4511	57992.25	3644.3850	2425.83	1218.5550	0.807888	4.834941	12.855742	2.600032	3.906093
15	2023-11-16	887	4013	59553.18	3705.2534	2444.11	1261.1434	0.923313	4.524239	14.840065	2.755479	4.177287
16	2023-11-17	956	4494	56309.40	3494.1469	2294.99	1199.1569	0.777514	4.700837	12.529907	2.400617	3.654965
17	2023-11-18	868	4383	48218.86	3145.2367	2092.52	1052.7167	0.717599	5.049539	11.001337	2.410737	3.623545
18	2023-11-19	449	1668	14858.15	913.1508	607.44	305.7108	0.547453	3.714922	8.907764	1.352873	2.033743
19	2023-11-20	442	1923	18274.51	1157.5725	766.17	391.4025	0.601962	4.350679	9.503125	1.733416	2.618942
20	2023-11-21	419	1702	14916.39	975.8979	642.85	333.0479	0.573383	4.062053	8.764036	1.534248	2.329112
21	2023-11-22	388	1397	15668.78	1103.4221	702.14	401.2821	0.789851	3.600515	11.216020	1.809639	2.843871
22	2023-11-23	857	3951	61900.30	3762.7734	2460.70	1302.0734	0.952360	4.610268	15.666996	2.871295	4.390634
23	2023-11-24	844	3618	49666.59	2968.2349	1942.93	1025.3049	0.820408	4.286730	13.727637	2.302050	3.516866
24	2023-11-25	703	2800	32140.58	2043.8839	1314.64	729.2439	0.729959	3.982930	11.478779	1.870043	2.907374
25	2023-11-26	748	2747	27327.43	1775.3034	1124.52	650.7834	0.646270	3.672460	9.948100	1.503369	2.373400
26	2023-11-27	739	3300	40838.19	2686.9941	1788.71	898.2841	0.814241	4.465494	12.375209	2.420447	3.635987
27	2023-11-28	704	3009	33251.68	2103.8334	1384.80	719.0334	0.699180	4.274148	11.050741	1.967045	2.988400
28	2023-11-29	905	3903	40426.15	2628.8506	1725.81	903.0406	0.673546	4.312707	10.357712	1.906972	2.904807
29	2023-11-30	904	3834	44285.07	2861.8577	1861.31	1000.5477	0.746442	4.241150	11.550618	2.058971	3.165772
30	2023-12-01	439	1575	19587.30	1281.7969	849.79	432.0069	0.813839	3.587699	12.436381	1.935740	2.919811
31	2023-12-02	406	1387	13021.53	849.4915	553.83	295.6615	0.612467	3.416256	9.388270	1.364113	2.092344
32	2023-12-03	391	1345	11118.85	733.8585	478.78	255.0785	0.545620	3.439898	8.266803	1.224501	1.876876
33	2023-12-04	457	1936	20871.47	1366.2389	907.95	458.2889	0.705702	4.236324	10.780718	1.986761	2.989582

	order_date	user_count	order_count	GMV_sum	revenue_sum	user_com_sum	gross_profit	avg_purchase_value	avg_purchase_frequency_rate	avg_purchase	avg_user_cashback	customer_value
0	2023-11-02	2	6	24.01	1.5900	0.80	0.7900	0.265000	3.000000	4.001667	0.400000	0.795000
1	2023-11-03	3	6	1035.64	52.7200	24.92	27.8000	8.786667	2.000000	172.606667	8.306667	17.573333
2	2023-11-04	2	3	163.82	11.4500	5.35	6.1000	3.816667	1.500000	54.606667	2.675000	5.725000
3	2023-11-05	3	7	41.42	2.9700	1.37	1.6000	0.424286	2.333333	5.917143	0.456667	0.990000
4	2023-11-06	4	30	113.97	7.7800	3.63	4.1500	0.259333	7.500000	3.799000	0.907500	1.945000
5	2023-11-07	9	133	264.03	16.6500	7.80	8.8500	0.125188	14.777778	1.985188	0.866667	1.850000
6	2023-11-08	8	60	244.97	17.0100	8.59	8.4200	0.283500	7.500000	4.082833	1.073750	2.126250
7	2023-11-09	9	169	237.49	16.2500	7.34	8.9100	0.096154	18.777778	1.405266	0.815556	1.805556
8	2023-11-10	7	33	208.12	13.8400	7.14	6.7000	0.419394	4.714286	6.306667	1.020000	1.977143
9	2023-11-11	42	271	1545.93	101.4000	48.18	53.2200	0.374170	6.452381	5.704539	1.147143	2.414286
10	2023-11-12	30	248	788.65	52.2536	24.44	27.8136	0.210700	8.266667	3.180040	0.814667	1.741787
11	2023-11-13	24	236	619.56	44.1305	19.39	24.7405	0.186994	9.833333	2.625254	0.807917	1.838771
12	2023-11-14	20	230	1180.46	80.3700	40.63	39.7400	0.349435	11.500000	5.132435	2.031500	4.018500
13	2023-11-15	30	287	830.85	48.4500	24.20	24.2500	0.168815	9.566667	2.894948	0.806667	1.615000
14	2023-11-16	20	265	1108.26	73.9000	34.68	39.2200	0.278868	13.250000	4.182113	1.734000	3.695000
15	2023-11-17	17	237	1085.12	60.7800	29.11	31.6700	0.256456	13.941176	4.578565	1.712353	3.575294
16	2023-11-18	16	253	402.05	25.9200	12.25	13.6700	0.102451	15.812500	1.589130	0.765625	1.620000
17	2023-11-19	7	186	124.78	7.9900	2.60	5.3900	0.042957	26.571429	0.670860	0.371429	1.141429
18	2023-11-20	12	211	437.31	29.6200	12.64	16.9800	0.140379	17.583333	2.072559	1.053333	2.468333
19	2023-11-21	9	192	349.07	22.7994	9.95	12.8494	0.118747	21.333333	1.818073	1.105556	2.533267
20	2023-11-22	9	27	237.57	17.3100	8.39	8.9200	0.641111	3.000000	8.798889	0.932222	1.923333
21	2023-11-23	24	80	972.17	67.5200	32.10	35.4200	0.844000	3.333333	12.152125	1.337500	2.813333
22	2023-11-24	24	94	776.45	50.7336	25.08	25.6536	0.539719	3.916667	8.260106	1.045000	2.113900
23	2023-11-25	15	33	167.69	11.5700	5.35	6.2200	0.350606	2.200000	5.081515	0.356667	0.771333
24	2023-11-26	23	68	566.73	35.7400	17.47	18.2700	0.525588	2.956522	8.334265	0.759565	1.553913
25	2023-11-27	12	30	203.23	14.4200	6.86	7.5600	0.480667	2.500000	6.774333	0.571667	1.201667
26	2023-11-28	11	84	1119.82	74.1200	37.98	36.1400	0.882381	7.636364	13.331190	3.452727	6.738182
27	2023-11-29	16	156	665.56	45.9900	23.11	22.8800	0.294808	9.750000	4.266410	1.444375	2.874375
28	2023-11-30	20	109	889.37	60.2900	30.42	29.8700	0.553119	5.450000	8.159358	1.521000	3.014500
29	2023-12-01	17	41	453.95	31.6600	16.11	15.5500	0.772195	2.411765	11.071951	0.947647	1.862353
30	2023-12-02	11	127	419.07	26.7700	12.71	14.0600	0.210787	11.545455	3.299764	1.155455	2.433636
31	2023-12-03	10	153	374.51	25.3300	12.65	12.6800	0.165556	15.300000	2.447778	1.265000	2.533000
32	2023-12-04	11	57	338.62	19.5100	10.38	9.1300	0.342281	5.181818	5.940702	0.943636	1.773636

	month	user_count	order_count	GMV_sum	revenue_sum	user_com_sum
0	1	1825	24630	269435.84	16750.1267	11201.22
1	2	1678	24097	252058.20	15544.1622	10396.27
2	3	1889	28799	311380.81	19079.2014	12704.55
3	4	1696	26607	233427.03	15142.1985	10103.48
4	5	1726	28864	268424.31	17121.4943	11286.16
5	6	1650	24047	289006.62	18242.3530	11899.79
6	7	1625	26190	303794.82	19714.5188	13110.60
7	8	1658	30433	387665.44	23739.9515	15943.00
8	9	1665	28331	325270.97	20669.6680	14022.45
9	10	1601	26682	282555.45	18627.2193	12555.74
10	11	1960	46767	497745.80	31265.4596	20842.87
11	12	1495	21896	251114.23	16050.4259	10827.01

	id	date	user_id	order_number	order_status	GMV	Revenue	offer_id	traffic_type	country	user_status
0	1207611460	2023-01-01	2175790	5095020934464829	completed	16.37	0.6136	28728	2	None	reactivated
1	1207611462	2023-01-01	2175790	5095020934424829	completed	4.34	0.0975	28728	2	None	reactivated
2	1207611463	2023-01-01	2175790	5095020934404829	completed	53.66	3.0660	28728	2	None	reactivated
3	1207611464	2023-01-01	2175790	5095020934394829	completed	39.64	2.2651	28728	2	None	reactivated
4	1207611592	2023-01-01	800601	8160713511228104	completed	6.26	0.4000	463	3	None	reactivated
...	...	...	...	...	...	...	...	...	...	...	...
170384	1210440694	2023-12-31	526730	5293804439164071	completed	1.76	0.1337	28728	3	None	reactivated
170385	1210445021	2023-12-31	2000822	5294102661345862	completed	9.50	0.2715	28728	3	None	reactivated
170386	1210587184	2023-12-31	71461	20411622640_c	rejected	0.09	0.0100	797	1	None	reactivated
170387	1210587207	2023-12-31	288802	20411645516_c	completed	3.66	0.3662	797	1	None	reactivated
170388	1210587208	2023-12-31	288802	20411614202_c	completed	0.10	0.0100	797	1	None	reactivated

	order_date	user_count	order_count	GMV_sum	revenue_sum	user_com_sum	gross_profit	avg_purchase_value	avg_purchase_frequency_rate	avg_purchase	avg_user_cashback	customer_value
0	2023-11-01	61	205	2644.46	183.3800	122.50	60.8800	0.894537	3.360656	12.899805	2.008197	3.006230
1	2023-11-02	48	160	1331.90	91.5300	59.64	31.8900	0.572063	3.333333	8.324375	1.242500	1.906875
2	2023-11-03	51	180	2565.07	134.9600	92.17	42.7900	0.749778	3.529412	14.250389	1.807255	2.646275
3	2023-11-04	54	185	1786.95	122.8338	79.24	43.5938	0.663966	3.425926	9.659189	1.467407	2.274700
4	2023-11-05	51	118	825.77	56.3938	36.56	19.8338	0.477914	2.313725	6.998051	0.716863	1.105761
5	2023-11-06	47	173	2137.47	139.0487	91.18	47.8687	0.803750	3.680851	12.355318	1.940000	2.958483
6	2023-11-07	55	157	3318.89	180.6200	120.15	60.4700	1.150446	2.854545	21.139427	2.184545	3.284000
7	2023-11-08	49	154	1716.36	109.9502	72.16	37.7902	0.713962	3.142857	11.145195	1.472653	2.243882
8	2023-11-09	46	148	1212.34	82.7000	56.26	26.4400	0.558784	3.217391	8.191486	1.223043	1.797826
9	2023-11-10	41	125	1903.40	133.0600	89.06	44.0000	1.064480	3.048780	15.227200	2.172195	3.245366
10	2023-11-11	240	1012	12648.86	766.6152	469.45	297.1652	0.757525	4.216667	12.498874	1.956042	3.194230
11	2023-11-12	132	519	5070.96	326.9062	204.16	122.7462	0.629877	3.931818	9.770636	1.546667	2.476562
12	2023-11-13	112	442	3064.49	209.9300	137.80	72.1300	0.474955	3.946429	6.933235	1.230357	1.874375
13	2023-11-14	106	381	3718.59	251.6549	156.54	95.1149	0.660512	3.594340	9.760079	1.476792	2.374103
14	2023-11-15	99	308	4941.16	324.5100	210.99	113.5200	1.053604	3.111111	16.042727	2.131212	3.277879
15	2023-11-16	101	332	3723.07	214.2185	140.74	73.4785	0.645236	3.287129	11.214066	1.393465	2.120975
16	2023-11-17	118	434	5437.32	353.7098	227.94	125.7698	0.815000	3.677966	12.528387	1.931695	2.997541
17	2023-11-18	95	439	3608.13	238.3500	155.18	83.1700	0.542938	4.621053	8.218975	1.633474	2.508947
18	2023-11-19	58	173	1014.28	69.5028	44.41	25.0928	0.401750	2.982759	5.862890	0.765690	1.198324
19	2023-11-20	59	171	2371.92	163.6500	110.48	53.1700	0.957018	2.898305	13.870877	1.872542	2.773729
20	2023-11-21	56	228	2219.16	152.7000	100.55	52.1500	0.669737	4.071429	9.733158	1.795536	2.726786
21	2023-11-22	43	183	1987.99	135.9400	91.18	44.7600	0.742842	4.255814	10.863333	2.120465	3.161395
22	2023-11-23	104	447	8146.40	437.6089	281.45	156.1589	0.978991	4.298077	18.224609	2.706250	4.207778
23	2023-11-24	97	314	4811.08	281.9800	186.04	95.9400	0.898025	3.237113	15.321911	1.917938	2.907010
24	2023-11-25	76	322	3027.45	197.2920	124.19	73.1020	0.612708	4.236842	9.402019	1.634079	2.595947
25	2023-11-26	82	290	2523.14	169.0146	107.71	61.3046	0.582809	3.536585	8.700483	1.313537	2.061154
26	2023-11-27	85	288	2647.78	174.6477	111.88	62.7677	0.606416	3.388235	9.193681	1.316235	2.054679
27	2023-11-28	88	381	5194.97	292.2500	193.83	98.4200	0.767060	4.329545	13.635092	2.202614	3.321023
28	2023-11-29	99	416	4117.39	281.5628	184.19	97.3728	0.676834	4.202020	9.897572	1.860505	2.844069
29	2023-11-30	105	341	4171.96	236.0200	149.98	86.0400	0.692141	3.247619	12.234487	1.428381	2.247810
30	2023-12-01	54	163	1195.90	82.7727	52.06	30.7127	0.507808	3.018519	7.336810	0.964074	1.532828
31	2023-12-02	37	92	1264.33	85.1200	53.40	31.7200	0.925217	2.486486	13.742717	1.443243	2.300541
32	2023-12-03	50	175	1539.47	86.9674	56.29	30.6774	0.496957	3.500000	8.796971	1.125800	1.739348
33	2023-12-04	43	265	2012.65	137.6900	91.37	46.3200	0.519585	6.162791	7.594906	2.124884	3.202093